Development of an Exposure Vulnerability Index Map Using GIS Modeling for Preliminary Seismic Risk Assessment in Sabah, Malaysia
Bibliographic record
Abstract
This research was conducted with a view to updating the management of earthquakes through an exposure vulnerability and potential seismic risk assessment, along with its application in Sabah (a state in East Malaysia). A set of indicators and methodologies has been proposed in this study with the goal of evaluating the level of exposure vulnerability and potential risk of certain locations to earthquake events at the local district scale. This study specifically involves the development of exposure vulnerability indicators; the statistical analysis method to standardize multivariate data together with a weight calculation of indicator variables; and a mathematical combination of different indicators for the development of the index map using the spatial analysis function of Geographical Information System (GIS) tools. Then, the derived exposure vulnerability index (EVI) map is overlaid with the seismic hazard in determining the geographical location of the most vulnerable areas and their exposure to seismic hazard events. As a result, and based on the available data, the exposure vulnerability index map shows that most districts in Sabah are at relatively low and moderate levels of risk except for a few districts, with several major cities in Sabah, such as Kota Kinabalu, Penampang, Sandakan and Tawau municipality, being situated at a high or very high exposure index. The combination of EVI maps and hazard maps indicate the dominance of the two factors influencing the potential level of earthquake risk. Studies reveal most of the southwest and central parts of the region are not at risk, as both exposure and hazard factors are at a low level. The proposed approach depicts an instrument for identifying cost-effective risk reduction initiatives by providing a scientific method for regional risk planning and management strategies. This research represents the first attempt to evaluate Sabah’s vulnerability to this type of natural disaster by understanding the spatial relationship between exposure vulnerability and earthquake hazard, which undoubtedly could be improved in several aspects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".